Research and development (R&D) (rd)

1.1. Contact organisation

Swiss Federal Statistical Office (FSO)

1.2. Contact organisation unit

Division WI (Economy),

Section WSA (Economic structure and analysis)

1.3. Contact name

Restricted from publication

1.4. Contact person function

Restricted from publication

1.5. Contact mail address

Office fédéral de la Statistique (OFS)

Espace de l'Europe 10

2010 Neuchâtel

SWITZERLAND

1.6. Contact email address

Restricted from publication

1.7. Contact phone number

Restricted from publication

1.8. Contact fax number

Not required 

31 October 2025

2.1. Metadata last certified

31 October 2025

2.2. Metadata last posted

31 October 2025

2.3. Metadata last update

31 October 2025

3.1. Data description

Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.

The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.

The guiding document to preparing the quality reports is the European Statistical System (ESS)  Handbook for Quality and Metadata Reports — re-edition 2021.

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.

3.2. Classification system

• The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
• The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
• The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD);
• The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011.

3.3. Coverage - sector

See below.

3.3.1. General coverage

Definition of R&D

R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.

The definition of R&D complies with the Frascati Manual.

3.3.2. Sector institutional coverage

Tertiary education institution	The higher education sector includes: 10 cantonal universities and 2 other university institutions (UNI)  2 Federal institutes of technology (FIT) 4 research institutes belonging to the two FITs (RI-FIT) 9 universities of applied sciences (UAS) 18 universitires of teacher education (UTE)
University and colleges: core of the sector	All covered
University hospitals and clinics	University hospitals are included in the higher education sector.
Inclusion of units that primarily do not belong to HES and the borderline cases	No

3.3.3. R&D variable coverage

R&D administration and other support activities	R&D administration and other support activities are part of R&D.
External R&D personnel	We do not ask for external R&D personnel
Clinical trials: compliance with the recommendations in the Frascati Manual §2.61.	Yes, we use the Frascati Manual criteria in Switzerland.

3.3.4. International R&D transactions

Receipts from rest of the world by sector - availability	available, but without the breakdown by sector
Payments to rest of the world by sector - availability	available, but without the breakdown by sector

3.3.5. Extramural R&D expenditures

According to the Frascati Manual (FM), expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).

Data collection  on extramural R&D expenditure (Yes/No)	No
Method for separating extramural R&D expenditure from intramural R&D expenditure	Not applicable
Difficulties to distinguish intramural from extramural R&D expenditure	Not applicable

3.4. Statistical concepts and definitions

See below.

3.4.1. R&D expenditure

Coverage of years	Calendar year (starting from reference year 2021 before it was only odd years)
Source of funds	Data are collected for each source of fund, in accordance with FM (2015)
Type of R&D	In accordance with FM (2015)
Type of costs	In accordance with FM (2015)
Defence R&D - method for obtaining data on R&D expenditure	We do not collect information on Defence R&D for the higher education sector

3.4.2. R&D personnel

See below.

3.4.2.1. R&D personnel – Head Counts (HC)

Coverage of years	Calendar year (starting from reference year 2021 before it was only odd years)
Function	In accordance with FM (2015)
Qualification	In accordance with FM (2015). We use the ISCED-2011 classification but have less detailed breakdowns than those recommanded by the FM. ISCED 6-8 : Tertiary level, universities ISCED 5 : Tertiary level, higher vocational education ISCED 1-4 : Other qualification
Age	Not available
Citizenship	In accordance with FM (2015), we collect the breakdown Swiss/Foreigner

3.4.2.2. R&D personnel – Full Time Equivalent (FTE)

Coverage of years	Calendar year (starting from reference year 2021 before it was only odd years)
Function	In accordance with FM (2015)
Qualification	In accordance with FM (2015). We use the ISCED-2011 classification but have less detailed breakdowns than those recommanded by the FM. ISCED 6-8 : Tertiary level, universities ISCED 5 : Tertiary level, higher vocational education ISCED 1-4 : Other qualification
Age	Not available
Citizenship	Not available

3.4.2.3. FTE calculation

For all the institution of the higher education sector (exept RI-FITs), the data is collected via a staff register (administrative data). Each universities is responsible for the data collection.

For the research institutes of the FIT domain, the calclation method is given in the annex of the questionnaire: "One full-time equivalence on R&D is the equivalent of one R&D employee working full-time for one year. Full-time equivalence on R&D is calculated by taking the type of workweek (full-time or part-time %), the duration of employment, and the portion of time devoted to R&D and multiplying these figures together".

We do not collect specific data post graduate students. They are included in R&D personnel only if they are  employed in the institution. In that case, there is no difference with the rest of R&D personnel.

3.5. Statistical unit

The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993

3.6. Statistical population

See below.

3.6.1. National target population

The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.

The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the HES Sector should consist of all R&D performing institutional units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level. 

	Target population when sample/census survey is used for collection of raw data	Target population when administrative data or pre-compiled statistics are used
Definition of the national target population	We cover all the higher education institutions in Switzerland.	We cover all the higher education institutions in Switzerland.
Estimation of the target population size	See §3.3	See §3.3

3.7. Reference area

Not requested.

3.8. Coverage - Time

Not requested

3.9. Base period

The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.

R&D expenditure is published in the following units:  Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).

R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.

2023

6.1. Institutional Mandate - legal acts and other agreements

See below.

6.1.1. European legislation

Legal acts / agreements:

Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.

The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.

 

Switzerland delivers R&D data on a volountary basis

6.1.2. National legislation

Existence of R&D specific statistical legislation	Yes Federal Law of 9 October 1992 on Federal Statistics (LSF) Federal Statistics Ordinance of 30 April 2025 (OFS)
Are respondents obliged by the national law to provide raw and administrative data:	Mandatory

6.1.3. Standards and manuals

• Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
• European Business Statistics Methodological Manual on R&D Statistics

6.2. Institutional Mandate - data sharing

Not requested.

7.1. Confidentiality - policy

A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.

At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009). 

At national level:  

a)       Confidentiality protection required by law:

Federal Statistics Act (FStatA) of 9 October 1992 (RS 431.01)

b)       Confidentiality commitments of survey staff:

Federal Statistics Ordinance of 30 April 2025 (OFS)

7.2. Confidentiality - data treatment

No micro data

8.1. Release calendar

The calendars of statistical publications are publicly available.

The data are available in February

 

8.2. Release calendar access

For Eurostat this is: Release calendar - Eurostat (europa.eu)

For Switzerland this is: Agenda | Federal Statistical Office - FSO

8.3. Release policy - user access

Statistical information shall be disseminated in such a way that all users can access it simultaneously. All users have access to statistical publications at the same time and under the same conditions, and any privileged pre-release access granted to an external user is limited, controlled and made public. Some authorities may receive advance information under embargo in order to prepare for possible questions. The policy on consultations and advance information regulates the modalities.

Source: LSF 18.1, Charte Principes fondamentaux 9 et 10, CoP 10 ind. 6

The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.

The frequency of R&D data dissemination in Switzerland for the HES sector is every year (since 2021, before it was every two years). Switzerland do not produce provisional data 

10.1. Dissemination format - News release

See below.

10.1.1. Availability of the releases

	Availability (Y/N)1	Links
Regular releases	Y	Almost CHF 26 billion invested in R&D in Switzerland in 2023 - Research and development in Switzerland in 2023 | Press release
Ad-hoc releases	N

1) Y - Yes, N – No

10.2. Dissemination format - Publications

See below.

10.2.1. Availability of means of dissemination

Means of dissemination	Availability (Y/N)1	Links
General publication/article	Y	Recherche et développement en Suisse 2023 - Finances et personnel | Publikation
Specific paper publication (e.g. sectoral provided to enterprises)	N

1) Y – Yes, N - No 

10.3. Dissemination format - online database

There is an indicator on R&D expenditure in the BES sector and related data tables on our website.

Système d'indicateurs Science et Technologie | Office fédéral de la statistique - OFS

10.3.1. Data tables - consultations

Not requested.

10.4. Dissemination format - microdata access

As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.

10.4.1. Provisions affecting the access

Access rights to the micro-data	Some micro data are available on the FSO website
Access cost policy	No cost for the available micro data
Micro-data anonymisation rules	No anonymisation

10.5. Dissemination format - other

See below.

10.5.1. Metadata - consultations

Not requested.

10.5.2. Availability of other dissemination means

Dissemination means	Availability (Y/N)1)	Micro-data / Aggregate figures	Comments
Internet: main results available on the national statistical authority’s website	Y	Aggregate figures	Science and Technologie indicator
Data prepared for individual ad hoc requests	Y	Aggregate figures	Data prepared for individual ad hoc request
Other	Y	Aggregate figures	Unregular paper publication

1) Y – Yes, N - No 

10.6. Documentation on methodology

see below

10.6.1. Metadata completeness - rate

Not requested.

10.7. Quality management - documentation

See below.

10.7.1. Documentation and users’ requests

Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.)	Graphs and analytical comments
Requests on further clarification, most problematic issues	Explanation on methodology

11.1. Quality assurance

At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).

 

At FSO level, the quality is insured by our methodological services (ongoing process)

11.2. Quality management - assessment

As it is an exhaustive data collection, mandatory for all the higher education institution the data quality is good.

12.1. Relevance - User Needs

See below.

12.1.1. Needs at national level

Users’ class1)	Description of users	Users’ needs
1-Institutions	OECD and ESTAT	All R-D statistics
1-Institutions	State Secretariat for Education, Research and Innovation (SERI). The SERI within the Federal Department of Home Affairs is the federal government's specialised agency for national and international matters concerning general and university education, research and space.	All the R-D and STI statistics needed for the redaction of the “Message relating to the encouragement of the formation, research and innovation” and for the strategic controlling of the formation, research and the technology objectives.
1-Institutions	State Secretariat for Economic Affairs (SECO).The SECO is the Confederation's competence centre for all core issues relating to economic policy.	All kind of R-D and STI statistics
2-Social actors	Economiesuisse: federation of the swiss companies	All the R-D statistics
3-Media	Media in general and in particular: “economic life, the review of economic policy”. Published under the auspices of the Secretariat for Economic Affairs SECO, this review: “economic life, the review of economic policy” analyzes every month the economic evolution of the country. Moreover, it regularly publishes statistical data of which R-D statistics.	All the R-D statistics
4- Researchers and students	Universities in general and in particular: the Swiss Institute for Business Cycle Research (KOF) within the Swiss Federal Institute of Technology of Zurich, (ETHZ). The KOF within the Swiss Federal Institute of Technology of Zurich supplies information in the range of the economic and market research.	R-D statistics for the validation of the Innovation survey.
4- Researchers and students	Researchers and students.	All kind of R-D and STI statistics.

1)       Users' class codification

1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.

2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.

3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.

4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)

5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)

6- Other (User class defined for national purposes, different from the previous classes.)

12.2. Relevance - User Satisfaction

To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.

12.2.1. National Surveys and feedback

Conduction of a user satisfaction survey or any other type of monitoring user satisfaction	The FSO conducts non-regular surveys on user satisfaction.
User satisfaction survey specific for R&D statistics	We do not conduct user satisfaction survey specific for R&D statistics.
Short description of the feedback received	We are in contact with our partners involved in the BES statistics. We receive good feedbacks

12.3. Completeness

See below.

12.3.1. Data completeness - rate

Switzerland provides data on a voluntary basis (No measure of this item)

12.3.2. Completeness - overview

Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.

 

	Reasons for missing cells
Preliminary variables	Switzerland do not produce preliminary variables
Obligatory data on R&D expenditure	Switzerland provides data on a voluntary basis (Variation coefficient too high or poor quality due to a too small number of R&D performers)
Optional data on R&D expenditure	Switzerland provides data on a voluntary basis (No measure of this item, variation coefficient too high or poor quality due to a too small number of R&D performers)
Obligatory data on R&D personnel	Switzerland provides data on a voluntary basis (Variation coefficient too high or poor quality due to a too small number of R&D performers)
Optional data on R&D personnel	Switzerland provides data on a voluntary basis (No measure of this item, variation coefficient too high or poor quality due to a too small number of R&D performers)
Regional data on R&D expenditure and R&D personnel	Switzerland provides data on a voluntary basis (No measure of this item)

12.3.3. Data availability

See below.

12.3.3.1. Data availability - R&D Expenditure

	Availability1)	Frequency of data collection	Gap years – years with missing data	Changes - Description	Changes - Year of introduction	Changes - Reasons
Source of funds	Y - 1996	every year	Before 2021 every two years
Type of R&D	Y - 1996	every year	Before 2021 every two years
Type of costs	Y - 1996	every year	Before 2021 every two years
Socioeconomic objective	N
Region	N
FORD	Y - 2000	every year	Before 2021 every two years	Switzerland has a non-attributable category, that doesn't existe in international comparison for the personnel by FORD.
Type of institution	Y	every year	Before 2021 every two years

1) Y-start year, N – data not available

12.3.3.2. Data availability - R&D Personnel (HC)

	Availability1)	Frequency of data collection	Gap years – years with missing data	Changes - Description	Changes - Year of introduction	Changes - Reasons
Sex	Y - 1996	every year	Before 2021 every two years
Function	Y - 1996	every year	Before 2021 every two years
Qualification	Y - 1996	every year	Before 2021 every two years			Switzerland doesn't have the same group category as used in international comparison for ISCED-11
Age	N
Citizenship	Y - 1996	every year	Before 2021 every two years
Region	N
FORD	Y - 2000	every year	Before 2021 every two years			The total FORD is different to the sum of the 6 FORD. In Switzerland we have a non-attributable category, that doesn't existe in international comparison for the personnel by FORD.
Type of institution	Y - 1996	every year	Before 2021 every two years

1) Y-start year, N – data not available

12.3.3.3. Data availability - R&D Personnel (FTE)

	Availability1)	Frequency of data collection	Gap years – years with missing data	Changes - Description	Changes - Year of introduction	Changes - Reasons
Sex	N
Function	Y - 1996	every year	Before 2021 every two years
Qualification	Y - 1996	every year	Before 2021 every two years			Switzerland doesn't have the same group category as used in international comparison for ISCED-11
Age	N
Citizenship	N
Region	N
FORD	Y - 2000	every year	Before 2021 every two years			The total FORD is different to the sum of the 6 FORD. In Switzerland we have a non-attributable category, that doesn't existe in international comparison for the personnel by FORD.
Type of institution	Y - 1996	every year	Before 2021 every two years

1) Y-start year, N – data not available

12.3.3.4. Data availability - other

Additional dimension/variable available at national level1)	Availability2)	Frequency of data collection	Breakdown variables	Combinations of breakdown variables	Level of detail
No other additional dimension /variable are available

1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').

2) Y-start year

12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)

Cross-classification	Unit	Frequency
Not available

13.1. Accuracy - overall

Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).

 

Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:

1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.

2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:

a) Coverage errors,

b) Measurement errors,

c) Non response errors and

d) Processing errors.

 

Model assumption errors should be treated under the heading of the respective error they are trying to reduce.

13.1.1. Accuracy - Overall by 'Types of Error'

	Sampling errors1)	Non-sampling errors1)				Model-assumption Errors1)	Perceived direction of the error2)
		Coverage errors	Measurement errors	Processing errors	Non response errors
Total intramural R&D expenditure	Not applicable (census and administrative data)
Total R&D personnel in FTE	Not applicable (census and administrative data)
Researchers in FTE	Not applicable (census and administrative data)

1)  Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.

2)  The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.

13.1.2. Assessment of the accuracy with regard to the main indicators

Indicators	5 (Very Good)1)	4 (Good)2)	3 (Satisfactory)3)	2 (Poor)4)	1 (Very poor)5)
Total intramural R&D expenditure	5
Total R&D personnel in FTE		4
Researchers in FTE		4

1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.  

2) 'Good' = If at least one out of the three criteria described above is not fully met.

3) 'Satisfactory' = If the average rate of response is lower than 60%, even by meeting the two remaining criteria.

4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not be met.

5) 'Very Poor' = If all the three criteria are not met.

13.2. Sampling error

That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.

13.2.1. Sampling error - indicators

See below.

13.2.1.1. Variance Estimation Method

Not applicable

13.2.1.2. Confidence interval for R&D expenditure by source of funds

Source of funds	R&D expenditure
Business enterprise	Not applicable
Government	Not applicable
Higher education	Not applicable
Private non-profit	Not applicable
Rest of the world	Not applicable
Total	Not applicable

13.2.1.3. Confidence interval for R&D personnel by occupation and qualification

		R&D personnel (FTE)
Occupation	Researchers	Not applicable
	Technicians	Not applicable
	Other support staff	Not applicable
Qualification	ISCED 8	Not applicable
	ISCED 5-7	Not applicable
	ISCED 4 and below	Not applicable

13.3. Non-sampling error

Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.

13.3.1. Coverage error

Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.

a)       Description/assessment of coverage errors:

No coverage errors it is based on administrative data and a census

 b)      Measures taken to reduce their effect:

Not applicable

13.3.1.1. Over-coverage - rate

Not requested.

13.3.1.2. Common units - proportion

Not requested.

13.3.2. Measurement error

Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.

a)       Description/assessment of measurement errors:

No measurement errors it is based on administrative data and a census

 b)      Measures taken to reduce their effect:

Not applicable

13.3.3. Non response error

Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.

There are two elements of non-response:

• Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
• Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.

The extent of response (and accordingly of non response) is also measured with response rates. 

13.3.3.1. Unit non-response - rate

The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.

Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.

Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100 

13.3.3.1.1. Un-weighted unit non-response rate

Number of units with a response in the survey	Total number of units in the survey	Unit non-response rate (Un-weighted)
Not applicable - census and administrative data

13.3.3.2. Item non-response - rate

Definition:
Un-weighted Item Non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample)] * 100

13.3.3.2.1. Un-weighted item non-response rate

	R&D Expenditure	R&D Personnel (FTE)	Researchers (FTE)
Item non-response rate (un-weighted) (%)	Not applicable	Not applicable	Not applicable
Comments	Not applicable	Not applicable	Not applicable

13.3.4. Processing error

Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.

13.3.4.1. Identification of the main processing errors

Data entry method applied	Not applicable
Estimates of data entry errors	Not applicable
Variables for which coding was performed	Not applicable
Estimates of coding errors	Not applicable
Editing process and method	Not applicable
Procedure used to correct errors	Not applicable

13.3.5. Model assumption error

Not requested.

14.1. Timeliness

Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.

14.1.1. Time lag - first result

Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)

 

a) End of reference period: Decembre 31st of the reference year

b) Date of first release of national data: February (Y+2)

c) Lag (days): 13 month

 

NB: we have only final results (no provisional results)

14.1.2. Time lag - final result

a) End of reference period: Decembre 31 of the reference year

b) Date of first release of national data: February (reference year +2)

c) Lag (days): 13 month

14.2. Punctuality

Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.

14.2.1. Punctuality - delivery and publication

Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)

14.2.1.1. Deadline and date of data transmission

	Transmission of provisional data	Transmission of final data
Legally defined deadline of data transmission (T+_ months)	10	18
Actual date of transmission of the data (T+x months)	We do not have provisional data	18
Delay (days)	Not applicable	0
Reasoning for delay

15.1. Comparability - geographical

See below.

15.1.1. Asymmetry for mirror flow statistics - coefficient

Not requested.

15.1.2. General issues of comparability

Not applicable

15.1.3. Survey Concepts Issues

The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197  or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.

Concept / Issues	Reference to recommendations	Deviation from recommendations	Comments on national definition / Treatment – deviations from recommendations
R&D personnel	FM2015 Chapter 5 (mainly sub-chapter 5.2).	No (only internal personal)
Researcher	FM2015, § 5.35-5.39.	No (only internal personal)
Approach to obtaining Headcount (HC) data	FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No (only internal personal)
Approach to obtaining Full-time equivalence (FTE) data	FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No (only internal personal)
Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel	FM2015, §5.25	No (only internal personal)
Intramural R&D expenditure	FM2015, Chapter 4 (mainly sub-chapter 4.2).	No
Statistical unit	FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No
Target population	FM2015 §9.6 (in combination with  Eurostat's EBS Methodological Manual on R&D Statistics).	No
Sector coverage	FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No
Post-secondary (non university / college) education institutions	FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	Excluded
Hospitals and clinics	FM2015 §9.13-9.17,  §9.109-9.112 (in combination with  Eurostat's EBS Methodological Manual on R&D Statistics).	No
Borderline research institutions	FM2015 §9.13-9.17,  §9.109-9.112 (in combination with  Eurostat's EBS Methodological Manual on R&D Statistics).	No
Major fields of science and technology coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18	No
Reference period	Reg. 2020/1197 : Annex 1, Table 18	No

15.1.4. Deviations from recommendations

The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.

Methodological issues	Reference to recommendations	Deviation from recommendations	Comments on national treatment / treatment deviations from recommendations
Data collection method	FM2015 Chapter 9 (mainly sub-chapter 9.5).	No
Survey questionnaire / data collection form	FM2015 Chapter 9 (mainly sub-chapter 9.5).	No
Cooperation with respondents	FM2015 Chapter 9 (mainly sub-chapter 9.5).	No
Coverage of external funds	FM2015 Chapter 9 (mainly sub-chapter 9.4).	No
Distinction between GUF and other sources – Sector considered as source of funds for GUF	FM2015 Chapter 9 (mainly sub-chapter 9.4).	No
Data processing methods	FM2015 Chapter 9 (mainly sub-chapter 9.5).	No
Treatment of non-response	FM2015 Chapter 9 (mainly sub-chapter 9.5).	Not applicable
Variance estimation	FM2015 Chapter 6 (mainly sub-chapter 6.9).	Not applicable
Method of deriving R&D coefficients	FM2015 Chapter 9 (mainly sub-chapter 9.5).	Not applicable
Quality of R&D coefficients	FM2015 Chapter 9 (mainly sub-chapter 9.5).	Not applicable
Data compilation of final and preliminary data	Reg. 2020/1197: Annex 1, Table 18	Not applicable

15.2. Comparability - over time

See below.

15.2.1. Length of comparable time series

See below.

15.2.2. Breaks in time series

	Length  of comparable time series	Break years1)	Nature of the breaks
R&D personnel (HC)	2000 onwars
Function	2000 onwars
Qualification	2000 onwars
R&D personnel (FTE)	2000 onwars
Function	2000 onwars
Qualification	2000 onwars
R&D expenditure	2000 onwars
Source of funds	2000 - 2012 2012 onwards	2012	New method for the calculation of the source of fund (based on administrative data)
Type of costs	2000 onwars
Type of R&D	2000 onwars
Other	2000 onwars

1)       Breaks years are years for which data are not fully comparable to the previous period.

15.2.3. Collection of data in the even years

Are the data produced in the same way in the odd and even years? If no, please explain the main differences.

15.3. Coherence - cross domain

This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual (FM) regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.

15.3.1. Coherence - sub annual and annual statistics

Not requested.

15.3.2. Coherence - National Accounts

Coherence with national accounts is insured

15.3.3. Coherence – Education statistics

Coherence with Education statistics is insured

15.4. Coherence - internal

See below.

15.4.1. Comparison between preliminary and final data

This part compares key R&D variables as preliminary and final data.

 

	Total R&D expenditure – HERD (in 1000 of national currency)	Total R&D personnel (in FTEs)	Total number of researchers  (in FTEs)
Preliminary data (delivered at T+10)	Not applicable	Not applicable	Not applicable
Final data (delivered T+18)	Not applicable	Not applicable	Not applicable
Difference (of final data)	Not applicable	Not applicable	Not applicable

Comments:

We do not have preliminary data

15.4.2. Consistency between R&D personnel and expenditure

	Average remuneration per year (cost in national currency)	Explanation of consistency issues if any
Consistency between FTEs of internal R&D personnel and R&D labour costs (1)	Not available
Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2)	Not available

(1)    Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).

(2)    Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).

The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible. 

16.1. Costs summary

	Costs for the statistical authority (in national currency)	Cost for the NSI in time use/person/day
Staff costs	Not available	Not available
Data collection costs	Not available	Not available
Other costs	Not available	Not available
Total costs	Not available	Not available

The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.

Comments on costs:

....

16.2. Components of burden and description of how these estimates were reached

	Value	Computation method
Number of Respondents (R)	Administrative data Only the 4 RI-FIT respond to the GOV survey
Average Time required to complete the questionnaire in hours (T)1)	Not asked for the 4 RI-FIT
Average hourly cost (in national currency) of a respondent (C)	Not asked for the 4 RI-FIT
Total cost	Not applicable

1)        T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)

17.1. Data revision - policy

Not requested.

17.2. Data revision - practice

Not requested.

17.2.1. Data revision - average size

Not requested.

18.1. Source data

Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.

18.1.1. Data source – general information

Administrative data (Finance and expenditure) for all the institutions of the higher education sector, except for the 4 RI-FIT who fill up the GOV survey

18.1.2. Sample/census survey information

Sampling unit	Institutional unit
Stratification variables (if any - for sample surveys only)	Not applicable
Stratification variable classes	Not applicable
Population size	see §3.3.2
Planned sample size	Not applicable
Sample selection mechanism (for sample surveys only)	Not applicable
Survey frame	Not applicable
Sample design	Not applicable
Sample size	Not applicable
Survey frame quality	Not applicable
Variables the survey contributes to	Not applicable

18.1.3. Information on collection of administrative data or of pre-compiled statistics

Source	Swiss federal statistical office (FSO)
Description of collected data / statistics	Cost accounting information and higher education personnal data
Reference period, in relation to the variables the administrative source contributes to	civil year
Variables the administrative source contributes to	Educational and higher education sector statistics

18.2. Frequency of data collection

See 12.3.3.

18.3. Data collection

See below.

18.3.1. Data collection overview

Information provider	Finances and costs of higher education institutions from Federal statistic office Switzerland For the 4 RI-FIT it is ARAMIS questionnaire of GOV sector
Description of collected information	cost accounting system and personnel data survey done by the higher education institution
Data collection method	Not applicable
Time-use surveys for the calculation of R&D coefficients	Not applicable
Realised sample size (per stratum)	Not applicable
Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.)	Not applicable
Incentives used for increasing response	Census no non responses
Follow-up of non-respondents	Census no non responses
Replacement of non-respondents (e.g. if proxy interviewing is employed)	Census no non responses
Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility)	Census no non responses
Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods)	Census no non responses

18.3.2. Questionnaire and other documents

Annex	Name of the file
R&D national questionnaire and explanatory notes in English:
R&D national questionnaire and explanatory notes in the national language:	RD_HESSI_A_CH_2023_0000_an_R&D_GOV_Survey_FR.pdf RD_HESSI_A_CH_2023_0000_an_R&D_GOV_Survey_DE.pdf
Other relevant documentation of national methodology in English:
Other relevant documentation of national methodology in the national language:

Annexes:
R&D_GOV_Survey_FR
R&D_GOV_Survey_DE

18.4. Data validation

• Outlier detection (early in the process).
• Checking the population coverage.
• Benchmark the responses (of a same unit) with the responses of the previous survey.

18.5. Data compilation

See below.

18.5.1. Imputation - rate

Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100/ (Total number of possible records for x)

18.5.2. Data compilation methods

Data compilation method - Final data	Not applicable
Data compilation method - Preliminary data	No preliminary data

18.5.3. Methodology for derivation of R&D coefficients

National methodology for their derivation.	Not applicable
Revision policy for the coefficients	Not applicable
Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc).	Not applicable

18.5.4. Measurement issues

Method of derivation of regional data	No regional data
Coefficients used for estimation of the R&D share of more general expenditure items	Not applicable
Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures	Not applicable
Treatment and calculation of GUF source of funds / separation from “Direct government funds”	Direct and indirect gov. funds distinction is made

18.5.5. Weighting and estimation methods

Description of weighting method	Not applicable
Description of the estimation method	Not applicable

18.6. Adjustment

Not requested.

18.6.1. Seasonal adjustment

Not requested.

Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.

The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.

The guiding document to preparing the quality reports is the European Statistical System (ESS)  Handbook for Quality and Metadata Reports — re-edition 2021.

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.

31 October 2025

See below.

The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993

See below.

Not requested.

2023

Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).

 

Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:

1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.

2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:

a) Coverage errors,

b) Measurement errors,

c) Non response errors and

d) Processing errors.

 

Model assumption errors should be treated under the heading of the respective error they are trying to reduce.

R&D expenditure is published in the following units:  Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).

R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.

See below.

Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.

The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.

The frequency of R&D data dissemination in Switzerland for the HES sector is every year (since 2021, before it was every two years). Switzerland do not produce provisional data 

Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.

See below.

See below.